Method and workstation for generating a joint-visualization image based on multiple functional imaging datasets

ABSTRACT

A method and workstation for combining multiple functional imaging datasets into a joint-visualization image. In one aspect, a method of functional imaging includes accessing a plurality of functional imaging datasets acquired from a volume-of-interest, wherein each of the plurality of functional imaging datasets is a different one of a plurality of functional imaging data types. The method includes registering the plurality of functional imaging datasets and determining a visualization priority for each of a plurality of pixels in a joint-visualization image based on a logical comparison of corresponding information in each of the plurality of functional imaging datasets. The method includes generating the joint-visualization image based on the visualization priority and at least a portion of each of the plurality of functional imaging datasets, wherein each of a plurality of pixels in the joint-visualization image represents only a single one of the plurality of functional imaging data types.

BACKGROUND

Functional imaging is a field of diagnostic imaging that focuses ondetecting and/or measuring changes in a patient related to blood flow orthe absorption of particular molecules. Molecular imaging, such asSingle-photon emission computed tomography (SPECT) and Positron emissiontomography (PET), and Functional magnetic resonance imaging (fMRI), arenon-limiting examples of functional imaging modalities.

In medical molecular imaging, an administered radiotracer is typicallybased on a ligand molecular or chemical component with an attachedradioactive isotope. The ligand is usually designed to mark, or beattached to, specific physiological components or processes in thepatient body, and the radioactive isotope emits a gamma-photon, oralternatively a positron, which in turn results in the emission of twogamma rays. The ligand molecule can be attached to a selected specificprotein. For example, certain proteins that are overexpressed in tumorsand metastasizes. The ligand may be based on molecules that are enteredinto cells as part of a specific physiological process such as glucosemetabolism. The ligand part of the tracer may be selected to flow in theblood circulation to enable the imaging of the vascular system. Theradioactive isotope may be chemically connected to the ligand molecule,or it can replace one or more of the atoms in the natural ligandmolecule to generate synthetic modified molecule (e.g., in ¹⁸F-FDG theFlourine-18 isotope replaces an oxygen-based atom group within a naturalglucose molecule). In some radiotracers, the radioactive isotope canalso serve as the ligand itself, such as in radioactive Iodine (e.g.,¹²³I) which is strongly absorbed by thyroid cells. Another example isthe ⁸²Rb isotope which is used for Cardiac PET imaging since it hasactivity very similar to that of a potassium ion and it is rapidlyextracted by the myocardium proportional to blood flow. In thesetracers, the ¹²³I or ⁸²Rb are administered as a salt-like molecule withsodium or chlorine atoms respectively. In addition, there are materialswhich are used at the same time for radiotherapy and for imaging such as¹⁷⁷Lu-PSMA.

Positron emission tomography (PET) is a functional imaging techniquethat involves administering a radiotracer (also known as a radionuclideor a radiopharmaceutical) to a patient and then generating images tovisualize the distribution and concentration of the radiotracer withinthe patient. Positron emission tomography (PET) is oftentimes used tohelp visualize biochemical changes within the patient’s body, such asthe patient’s metabolism. Each PET radiotracer includes an organicligand. Each PET radiotracer is configured to emit positrons. Positronemission tomography (PET) images the distribution of the radiotracerbased on the positrons emitted from the positron-emitting isotope. Thepositrons interact with electrons in the patient’s body, releasing gammarays which are detected by the detectors of the PET imaging system.

One of the most common PET radiotracers is fluorodeoxyglucose (¹⁸F-FDG)that includes a fluorine-18 (¹⁸F) as the positron-emitting isotope.However, there any other PET radiotracers in addition to ¹⁸F-FDG thatmay include one or both of a different positron-emitting isotope or adifferent ligand. Two examples of other PET radiotracers include⁶⁸Ga-PSMA-11 and ⁶⁸Ga-DOTATOC. Both ⁶⁸Ga-PSMA-11 and ⁶⁸Ga-DOTATOC usegallium-68 (⁶⁸Ga) as the positron-emitting isotope instead offluorine-18 (¹⁸F). Additionally, the ⁶⁸Ga is bound to a different ligandin both ⁶⁸Ga-PSMA-11 and ⁶⁸Ga-DOTATOC compared to ¹⁸F-FDG. Someradiotracers may be more specific or sensitive than others to specificdiseases, organs, physiological situations, or medical conditions. Assuch, using more than one type of radiotracer may prove advantageouswhen imaging and diagnosing a patient due to the different sensitivitiesof the various radiotracers.

Single photon emission computed tomography (SPECT) is a functionalimaging technique that involves administering a radiotracer to thepatient and then detecting radiation emitted from the radiotracer inorder to generate an image. Examples of radiotracers commonly used inSPECT imaging include Iodine-123 (¹²³I), ^(99m)Tc-MDP, and^(99m)Tc-MIBI. As with PET imaging, the various different radiotracersused for SPECT imaging may each be more specific or sensitive to certaindiseases, organs, physiological situations, or medical conditions. Itmay, therefore, be advantageous to utilize more than one SPECTradiotracer in order to improve the diagnosis of a patient.

Functional magnetic resonance imaging (fMRI) is another type offunctional imaging that may be used to obtain functional images of apatient. Instead of relying on a radiotracer, like both PET and SPECT,fMRI instead images the patient using a strong magnetic field andradio-frequency energy. An fMRI image may be more specific or sensitiveto specific diseases, organ, physiological situations or medicalconditions than either PET imaging or SPECT imaging. Due to the varioussensitivities, etc. of the various types of functional images, it may bedesirable to visualize information representing various differentfunctional imaging data types into a single image. Conventionaltechniques have attempted to visualize multiple different functionalimaging data types at the same time in a single image. For example,conventional techniques have registered images representing differenttypes of functional imaging data into a single fused image. In someconventional approaches, a first type of functional imaging data will berepresented in a first color and a second type of functional imagingdata will be represented in a second color. In some conventionalapproaches, a first functional image (represented in a first color) isoverlaid with a second functional image (represented in a second color)and displayed in a single fused image. However, it is typically verydifficult to achieve satisfactory visual clearness by simply overlayingor blending the first functional image with the second functional image.Adding multiple layers of information, that are displayed at the sametime, may create visual confusion for the user and may result in thegeneration or creation of undesired hues/colors when the firstfunctional image and the second functional image are combined/blendedinto the fusion image.

For these and other reasons there is a need for an improved method andworkstation for functional imaging in order to generate and display ajoint-visualization image representing a plurality of functional imagingdatasets.

BRIEF DESCRIPTION

A summary of certain embodiments disclosed herein is set forth below. Itshould be understood that these aspects are presented merely to providethe reader with a brief summary of these certain embodiments and thatthese aspects are not intended to limit the scope of this disclosure.Indeed, this disclosure may encompass a variety of aspects that may notbe set forth below.

In one embodiment, a method of functional imaging includes accessing aplurality of functional imaging datasets acquired from avolume-of-interest, wherein each of the plurality of functional imagingdatasets is a different one of a plurality of functional imaging datatypes. The method includes registering the plurality of functionalimaging datasets to each other. The method includes determining avisualization priority for each of a plurality of pixels in ajoint-visualization image based on logical comparison of correspondinginformation in each of the plurality of functional imaging datasets,wherein the visualization priority defines which one of the plurality offunctional imaging data types will be represented by each of theplurality of pixels. The method includes generating thejoint-visualization image based on the visualization priority determinedfor each of the plurality of pixels and at least a portion of each ofthe plurality of functional imaging datasets, wherein thejoint-visualization image represents information from each of theplurality of functional imaging data types at the same time, and whereineach of the plurality of pixels in the joint-visualization imagerepresents only a single one of the plurality of functional imaging datatypes. The method includes displaying the joint-visualization image on adisplay device.

In another embodiment, a workstation includes a display device and aprocessor. The processor is configured to access a plurality offunctional imaging datasets acquired from a volume-of-interest, whereineach of the plurality of functional imaging datasets is a different oneof a plurality of functional imaging data types. The processor isconfigured to register the plurality of functional imaging datasets toeach other. The processor is configured to determine a visualizationpriority for each of a plurality of pixels in a joint-visualizationimage based on a logical comparison of corresponding information in eachof the plurality of functional imaging datasets, wherein thevisualization priority defines which one of the plurality of functionalimaging data types will be represented by each of the plurality ofpixels. The processor is configured to generate the joint-visualizationimage based on the visualization priority determined for each of theplurality of pixels and at least a portion of each of the plurality offunctional imaging datasets, wherein the joint-visualization imagerepresents information from each of the plurality of functional imagingdata types at the same time, and wherein each of the plurality of pixelsin the joint-visualization image represents only a single one of theplurality of functional imaging data types. The processor is configuredto display the joint-visualization image on the display device.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the present subjectmatter will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic representation of a workstation in accordance withan embodiment;

FIG. 2 is an exploded representation of a touchscreen in accordance withan embodiment;

FIG. 3 is a flowchart of a method in accordance with an embodiment;

FIG. 4 is a schematic representation of a hybrid imaging system inaccordance with an embodiment;

FIG. 5 is a schematic illustration of a patient from a side-view inaccordance with an embodiment;

FIG. 6 is a schematic illustration of the patient from a top-view inaccordance with an embodiment;

FIG. 7 is a schematic representation of a patient image volume inaccordance with an embodiment;

FIG. 8 is a flowchart of a method in accordance with an embodiment;

FIG. 9 is a schematic representation of a portion of a method inaccordance with an embodiment;

FIG. 10 is a flowchart of a method in accordance with an embodiment;

FIG. 11 a representation of a joint-visualization image in accordancewith an embodiment;

FIG. 12 is a representation of a side view of the patient image volumewith respect to a joint-visualization image in accordance with anembodiment;

FIG. 13 is a schematic representation of a patient image volume and animage plane in accordance with an embodiment;

FIG. 14A is a representation of a perspective view of a single raythrough a patient image volume with respect to the joint-visualizationimage in accordance with an embodiment;

FIG. 14B is a representation of a perspective view of a single raythrough a patient image volume with respect to the joint-visualizationimage in accordance with an embodiment;

FIG. 15 is a schematic illustration of a fusion image in accordance withan exemplary embodiment; and

FIG. 16 is a flow chart of a method in accordance with an embodiment;

DETAILED DESCRIPTION

One or more specific embodiments will be described below. In an effortto provide a concise description of these embodiments, all features ofan actual implementation may not be described in the specification. Itshould be appreciated that in the development of any such actualimplementation, as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers’ specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present subjectmatter, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Furthermore, any numerical examples in the following discussion areintended to be non-limiting, and thus additional numerical values,ranges, and percentages are within the scope of the disclosedembodiments.

FIG. 1 depicts a workstation 100 in accordance with an embodiment. Theworkstation 100 shown in FIG. 1 includes a processor 102, a userinterface 104, a display device 106, and a memory 108. The memory 108may be random access memory (RAM) according to an embodiment. Theprocessor 102 may include a CPU according to an embodiment. According toother embodiments, the processor 102 may include other electroniccomponents capable of carrying out processing functions, such as a GPU,a microprocessor, a DSP, a field-programmable gate array (FPGA), or anyother type of processor capable of performing logical operations.According to other embodiments, the processor 102 may include multipleelectronic components capable of carrying out processing functions. Forexample, the processor 102 may include two or more electronic componentsselected from a list of electronic components including: a CPU, a DSP,an FPGA, and a GPU.

The user interface 104 may be used to control operation of theworkstation 100. The user interface 104 may be used to control the inputof patient data, or to select various modes, operations, parameters, andthe like. The user interface 104 may include one or more user inputdevices such as a keyboard, hard keys, a touch pad, a track ball, rotarycontrols, sliders, soft keys, or any other user input devices. Accordingto some embodiments, the user interface 104 may include a touch panelthat is part of a touchscreen. An exemplary touchscreen will bedescribed hereinafter with respect to FIG. 2 .

The workstation 100 includes a display device 106. The display device106 may include any type of display screen or display that is configuredto display images, text, graphical user interface elements, etc. Thedisplay device 106 may be, for example, a cathode ray tube (CRT)display, a light-emitting diode (LED) display, an organic light-emittingdiode (OLED) display, a liquid crystal display (LCD), etc. According tosome embodiments, the display device 106 may be a display screen that isa component of a touchscreen.

As discussed above, the display device 106 and the user interface 104may be components in a touchscreen. FIG. 2 is an exploded representationof a touchscreen 122 in accordance with an exemplary embodiment. Thetouchscreen 122 includes a touch panel 126 and a display screen 128 inaccordance with an embodiment. The touch panel 126 may be located behindthe display screen 128 or in front of the display screen 128 accordingto various non-limiting examples. For embodiments where the touch panel126 is positioned in front of the display screen 128, the touch panel126 may be configured to be substantially transparent so that the usermay see images displayed on the display screen 128. The touch panel 126may utilize any type of technology configured to detect a touch orgesture applied to the touch panel 126 of the touchscreen 122. Asdiscussed hereinabove, the display device 118 may include a displayscreen of a touchscreen such as the display screen 128, and the userinterface 115 may include a touch panel, such as the touch panel 126 ofthe touchscreen 122. The touch panel 126 may be configured to detectsingle-point touch inputs and/or multipoint touch inputs according tovarious embodiments. The touch panel 126 may utilize any type oftechnology configured to detect a touch or gesture applied to the touchpanel 126 of the touchscreen 122. For instance, the touch panel 126 mayinclude resistive sensors, capacitive sensors, infrared sensors, surfaceacoustic wave sensors, electromagnetic sensors, near-field imagingsensors, or the like. Some embodiments may utilize the touch panel 126of the touchscreen 122 to provide all of the user interfacefunctionalities for the workstation 100, while other embodiments mayalso utilize one or more other components as part of the user interface104. The processor 102 may be configured to access one or morefunctional imaging datasets.

FIG. 3 illustrates a flowchart of an embodiment of a method 300. Themethod 300 shown in FIG. 3 may be performed with a workstation, such asthe workstation 100 shown in FIG. 1 . The technical effect of the method300 is the display of joint-visualization image on the display device106. The joint-visualization image represents information from aplurality of functional imaging data types at the same time. FIG. 3 willbe described according to an exemplary embodiment using the workstation100 shown in FIG. 1 .

At step 302, the processor 102 accesses a plurality of functionalimaging datasets acquired from a volume-of-interest. The workstationmay, for instance, access the plurality of functional imaging datasetsfrom a memory or storage, from a Picture archiving and communicationsystem (PACS), from a local server, from a remote server, or directlyfrom one or more imaging systems. Each of the plurality of functionalimaging datasets is a different one of a plurality of functional imagingdata types. A first functional imaging dataset 110 and a secondfunctional imaging dataset 112 are schematically represented withrespect to the workstation 100. According to an exemplary embodiment,the processor 102 may be configured to access the first functionalimaging dataset 110 and the second functional imaging dataset 112.According to other embodiments, the processor 102 may be configured toaccess more than two different functional imaging datasets. The data inone or more of the functional datasets may include quantitative values,which may be in either physical units or calibrated according to astandard calibrated scale, or one or more of the functional imagingdatasets may include values in an arbitrary scale. The arbitrary scalemay, for instance, be determined based on the image acquisitionparameters and/or the amount of radiopharmaceutical administered to thepatient. Additional information about the functional imaging datasetswill be discussed hereinbelow.

Each of the plurality of functional imaging datasets is a differentfunctional imaging data type. The functional imaging data type may referto one or both of a functional imaging modality used to acquire eachrespective functional imaging dataset or the radiotracer used to acquireeach respective functional imaging dataset. Positron emission tomography(PET) imaging, single photon computed emission tomography (SPECT)imaging, and functional magnetic resonance imaging (fMRI) arenon-limiting examples of different functional imaging modalities. Apositron emission tomography (PET) dataset is acquired with a positronemission tomography (PET) imaging system; a single photon computedemission tomography (SPECT) dataset is acquired with a single photoncomputed emission tomography (SPECT) imaging system; and a functionalmagnetic resonance imaging (fMRI) dataset is acquired with a functionalmagnetic resonance imaging (fMRI) imaging system. For purposes of thisdisclosure, a first functional imaging dataset acquired with a differentfunctional imaging modality than a second functional imaging dataset isconsidered to be a different functional imaging data type than thesecond functional imaging dataset. For example, a first functionalimaging dataset acquired with a Positron emission tomography (PET)imaging system is a different functional imaging data type than a secondfunctional imaging dataset acquired with either a positron emissiontomography (PET) imaging system using a different radiotracer, a singlephoton computed emission tomography (SPECT) imaging system or afunctional magnetic resonance imaging (fMRI) imaging system.

According to various embodiments, one or more of the plurality offunctional imaging datasets acquired at step 302 may have been acquiredwith a hybrid, or multi-modality imaging system. Examples of hybrid, ormulti-modality, imaging systems include, but are not limited to a PET-CTimaging system, a SPECT-CT imaging system, a PET-MRI imaging system, aSPECT-MRI imaging system, and an MRI-CT imaging system. According tovarious embodiments, the hybrid imaging systems involving an MRI imagingsystem, such as the PET-MRI imaging system, the SPECT-MRI imagingsystem, and the MRI-CT imaging system may use an fMRI imaging systemconfigured to obtain MRI data that is functional imaging data.

FIG. 4 is a schematic representation of a hybrid imaging system 400. Thehybrid imaging system 400 includes a patient support 402 supporting abed 404. The hybrid imaging system 400 includes a first imaging system406 and a second imaging system 408. According to an exemplaryembodiment, the first imaging system 406 and the second imaging system408 may share a common bore. A bore centerline 410 is shown in FIG. 4 .The patient bed 404 is configured to be translated into the bore of thehybrid imaging system 400.

The first imaging system 406 may be a first type of imaging system, suchas an MRI imaging system, a CT imaging system, a SPECT imaging system,or a PET imaging system. The second imaging system 408 may be adifferent type of imaging system. The second imaging system 408 may, forinstance, be an MRI imaging system, a CT imaging system, a SPECT imagingsystem, or a PET imaging system. According to an embodiment, the firstimaging system 406 may be a functional imaging system, such as a SPECTimaging system, a PET imaging system, or a fMRI imaging system. And thesecond imaging system 408 may be an anatomical imaging system, such as aCT imaging system or an MRI imaging system.

According to various embodiments, the relative positioning of theanatomical imaging system and the functional imaging system may beswitched. For example, the first imaging system may be an anatomicalimaging system, such as a CT imaging system or an MRI imaging system.And, the second imaging system may be a functional imaging system, suchas a SPECT imaging system, a PET imaging system, or a fMRI imagingsystem.

Each of the plurality of functional imaging datasets accessed at step302 may have been acquired with a different type of imaging system, andeach functional imaging dataset may represent a different functionalimaging data type. For example, if a first of the plurality offunctional imaging datasets was acquired with a SPECT imaging system, asecond of the functional imaging datasets may have been acquired with adifferent type of functional imaging system, such as a PET imagingsystem or an fMRI imaging system. It should be appreciated by thoseskilled in the art that other embodiments may use functional imagingdatasets that were acquired with imaging systems other than a PETimaging system, a SPECT imaging system, and an fMRI imaging system.

According to various embodiments, two or more of the functional imagingdatasets may have been acquired with the same type of functional imagingsystem. For example, two or more of the functional imaging datasets mayhave been acquired with a PET imaging system, and/or two or more of thefunctional imaging datasets may have been acquired with a SPET imagingsystem. Both PET and SPECT imaging involves the use of a radiotracer.Most radiotracers includes a radioactive isotope bound to an organicligand, which serves as a targeting agent. The ligand in eachradiotracer is selected to interact with specific proteins. Cliniciansmay choose a radiotracer with a specific ligand in order to specificallytarget various proteins within the patient.

According to an embodiment where the plurality of functional imagingdatasets were acquired with a PET imaging system, a first of theplurality of functional imaging datasets may have been acquired using afirst radiotracer including a first ligand, and a second of theplurality of functional imaging datasets may have been acquired using asecond radiotracer including a second ligand that is different than thefirst ligand.

According to an embodiment where the plurality of functional imagingdatasets were acquired with a SPECT imaging system, a first of theplurality of functional imaging datasets may have been acquired using afirst radiotracer including a first ligand, and a second of theplurality of functional imaging datasets may have been acquired using asecond radiotracer including a second ligand that this different thanthe first ligand.

Next, at step 304, the processor 102 registers the plurality offunctional imaging datasets to each other. According to embodimentswhere the functional datasets were acquired using a hybrid imagingsystem, such as the hybrid imaging system 400 shown in FIG. 4 , eachfunctional imaging dataset may already be registered to an anatomicaldataset. For instance, each functional imaging dataset may be registeredto a CT dataset. Registering a functional image dataset to an anatomicalimage dataset, when both datasets were acquired on the same hybridimaging system, is generally a relatively easy task since the twodatasets were acquired at times that were temporally close to each otherand because the patient was in the same, or a very similar, positionduring the acquisition of both datasets. For most hybrid imagingsystems, the functional imaging dataset may be registered to theanatomical imaging dataset using a rigid transformation, such as one ormore linear translations and/or rotations. However, fully elastic imageregistration techniques may be used as well and are known in the art.

According to an embodiment, a first anatomical image dataset may befull-dose CT dataset, while a second anatomical image dataset may be alow-dose CT dataset. For example, the second anatomical image datasetmay be acquired using a scout scan or other type of low-dose CTacquisition protocol to reduce the total dose received by the patient.Acquiring one or more low-dose CT datasets may be advantageous since thelow-dose CT dataset works perfectly well for registration purposes andthe first anatomical image dataset is a full-dose CT dataset.

Next, at step 306, the processor 102 determines a visualization priorityfor each pixel in a joint-visualization image. The joint-visualizationimage includes a plurality of pixels. FIG. 5 represents a schematicillustration of a patient 500 from a side-view and FIG. 6 represents aschematic illustration of the patient 500 from a top-view. Avolume-of-interest 502 is represented on both FIG. 5 and FIG. 6 . Theplurality of functional imaging datasets accessed by the processor 102at step 302 have been acquired from the volume-of-interest 502 accordingto an exemplary embodiment. It should be appreciated that the pluralityof imaging datasets may be acquired from a different volume-of-interestaccording to various embodiments. For example, the volume-of-interestmay have a one or both of a different shape than the volume-of-interest502 and/or be located in a different anatomical position with respect tothe patient 500. Those skilled in the art will appreciate that theposition of the volume-of-interest 500 will depend on the region oranatomy of the patient that is being imaged.

FIG. 7 shows a representation of a patient image volume 700. Accordingto an embodiment, the information in the patient image volume 700 mayultimately be rendered and displayed as a joint-visualization image.This technique may be used when, for example, rending and displaying amultiplanar reformation image. The patient image volume 700 includes aplurality of voxels 702. The patient image volume 700 is schematicallyrepresented as a 10 × 10 × 10 voxel cube, but it should be appreciatedthat the patient image volume for most applications will includesignificantly more voxels and that the patient image volume 700 isschematically represented as a 10 × 10 × 10 voxel cube to help explainvarious embodiments. FIG. 7 includes a coordinate axis 704 thatrepresents an x-direction with an x-axis 706, a y-direction with ay-axis 708, and a z-direction with a z-axis 710.

At step 306, the processor 102 determines a visualization priority foreach of the plurality of pixels within a joint-visualization image. Thevisualization priority defines which one of the plurality of functionalimaging datasets will be represented by each of the plurality of pixelsin the joint-visualization image. According to an embodiment of themethod 300, each of the plurality of pixels in the joint-visualizationimage will represent information from only one of the plurality offunctional imaging datasets. According to an embodiment, the processor102 may individually determine, on a pixel-by-pixel basis, which one ofthe plurality of functional imaging datasets will be represented by eachof the plurality of pixels in the joint-visualization image.

According to exemplary embodiments, the processor 102 may be configuredto determine the visualization priority for each of the pixels based ona logical comparison of corresponding information from each of theplurality of functional imaging datasets. Corresponding informationincludes information from two or more of the functional imaging datasetsthat was acquired from within the same sub-volume within thevolume-of-interest 502. For example, determining visualization prioritybased on a logical comparison of corresponding information may includecomparing one or more first voxel values that are part of a firstfunctional imaging dataset with one or more second voxel values that arepart of a second functional imaging dataset. The first voxel values andthe second voxel values may both have been acquired from the samesub-volumes within the volume-of-interest. For example, according tovarious embodiments, the corresponding information may include theportion of each functional imaging dataset that would be used todetermine a value for a respective pixel. The number of voxel valuesused to determine the value of a single pixel may depend upon the typeof image that is displayed. The value of each pixel may be based on acombination of multiple voxel values, a maximum or a minimum voxel valuealong a ray, or a single voxel value according to various embodiments.

As discussed previously with respect to the method 300, the functionalimaging datasets may include quantitative data (including quantitativevalues) or non-quantitative data (including values that are notquantitative). According to some embodiments, one or more of thefunctional imaging datasets may be a quantitative dataset includingquantitative values in physical units or according to standardcalibrated scale. For example, the medical imaging system used toacquire the functional imaging dataset may calibrate the acquired dataso that functional imaging dataset accessed by the processor 102 is aquantitative dataset. According to some embodiments where the acquireddata is not calibrated by the medical imaging system, the processor 102may be configured to convert the acquired data into quantitative dataincluding calibrated values. For example, the processor 102 may convertone or more of the functional imaging datasets according to a standardcalibrated scale, such as the standardized uptake value (SUV) scale. Thestandardized uptake value (SUV), which is also referred to as a doseuptake ratio, is the ratio of radiotracer concentration at a certaintime normalized to injected does per unit of patient body mass. SUV istypically calculated using the following formula:

$SUV = \frac{Radiotracer\mspace{6mu} Activity\left( \frac{MBq}{mL} \right)}{{injected\mspace{6mu} Dose\left( {MBq} \right)}/\left( {Patient\mspace{6mu} mass(g) \times 1\left( \frac{mL}{g} \right)} \right)}$

The SUV scale provides an easy way to compare/evaluate measuredfunctional imaging data with functional imaging data from previousstudies. According to other embodiments, some or all of the plurality offunctional imaging datasets may have already been converted into the SUVscale prior to being accessed by the processor 102 at step 302.

According to various embodiments, the processor 102 may determine thevisualization priority for each pixel in the j oint-visualization imageby first determining a visualization priority for each of a plurality ofvoxels in a patient image space. This may be used, for example, whengenerating a multiplanar reformation image. According to an embodiment,the processor 102 may perform a logical comparison of the functionalimaging data corresponding to each of the plurality of voxels using oneor more logical rules stored in, for example, the memory 108. Thelogical rules may include one or more thresholds or conditions thatcollectively determine the functional imaging data type that will berepresented by each of the voxels. For example, the logical rules mayinclude one or more “if-then” logical conditions and/or thresholds thatare used to determine the functional imaging data type that will berepresented by each of the voxels. For example, the logical rules mayinclude rules that indicate that each voxel should represent thefunctional imaging data type with the highest standard uptake value(SUV) value. Or the logical rules may include rules that are weightedtowards one of the functional imaging data types. For example, thelogical rules may apply a weighting factor (such as 0.25, 0.5, 0.75,1.25, 1.5, 2, etc.) to values associated with one or more of thefunctional imaging data types and then determining that the voxel shouldrepresent the functional imaging data type with the highest value afterscaling has been applied to the SUV value/s of one or more functionalimaging data types. The logical rules may also include rules toestablish priority between functional imaging datasets acquired withdifferent imaging modalities. For example, the logical rules mayestablish one or more thresholds for determining the visualizationpriority when the plurality of functional imaging datasets were acquiredwith two or more different imaging modalities. For example, the logicalrules may establish one or more thresholds to determine visualizationpriority between the various functional imaging data types acquired withdifferent imaging modalities. The logical rules may establish rules orthresholds to determine which one of the functional imaging data typesshould be represented by each of the plurality of voxels. For example,the logical rules may establish a way to compare data from fMRI with SUVdata acquired with a PET imaging system or a SPECT imaging system.

According to various exemplary embodiments, the setting of visualizationpriority for embodiments involving multiple modalities may involve athreshold, similar to the examples described previously, or it mayinvolve a ratio of two values relative to one or more thresholds. Forexample, a threshold can be determined for SUV values for PET, or athreshold may be determined with respect to physical perfusion value fordynamic contract fMRI. The visualization priority may be based on one ormore of the value (i.e., SUV physical perfusion value, etc.) or a ratioof two of the values. Additionally, the processor 102 may use a logicalset of rules to establish visualization priority. According to anexample involving both PET and fMRI, if the PET SUV value is above afirst threshold, then PET (i.e., the PET functional imaging data type)may be assigned visualization priority. If the SUV value is below asecond threshold and the perfusion is above a third threshold, then fMRI(i.e., the fMRI functional imaging data type) may be assignedvisualization priority. It should be appreciated that this is just anexemplary embodiment and that any different logical rule or anydifferent set of logical rules may be used to determine visualizationpriority. Additionally, while an example was described with respect toPET and fMRI, it should be appreciated that logical rules may be used todetermine visualization priority for any two or more functional imagingdata types according to various embodiments. The processor may beconfigured to determine the visualization priority for each of theplurality of pixels in the joint-visualization image differentlyaccording to various embodiments. For example, other embodiments may notinvolve determining a visualization priority for each of the pluralityof voxels in a patient image volume. According to various otherembodiments, the processor 102 may be configured to determine thevisualization priority based on a logical comparison of correspondingvoxel data from two or more of the functional imaging datasets.Additional details will be described hereinafter.

FIG. 8 illustrates a flowchart of an embodiment of a method 750. Thetechnical effect of the method 750 is the display of joint-visualizationimage on the display device 106. The joint-visualization imagerepresents information from a plurality of different functional imagingdata types at the same time. Step 764, 766, 768, 770, and 772 of themethod 750 may be performed with a workstation, such as the workstation100 shown in FIG. 8 .

At step 752, a clinician administers a first radiotracer to the patient.The first radiotracer includes a first ligand. According to an exemplaryembodiment, the first radiotracer may be ⁶⁸Ga-FAPI. At step 754, a firstCT dataset of the patient is acquired. And at step 756, a first PETdataset is acquired from a volume-of-interest in the patient. Steps 754and 756 may be performed with a PET-CT hybrid imaging system, forexample.

Next, at step 758, the clinician administers a second radiotracer to thepatient. The second radiotracer includes a second ligand. According toan exemplary embodiment, the second radiotracer may be ¹⁸F-FDG.According to various embodiments, it may be advantageous to use a firstradiotracer with a higher specificity to the suspected disease. Forexample, depending upon the goal of the study, ⁶⁸Ga-FAPI may have ahigher specificity to the suspected disease than ¹⁸F-FDG. Since thereare no other radiotracers in the patient when the first radiotracer isadministered, the measured signals (i.e., counts) from the firstradiotracer are always accurate. However, the signals (i.e., counts)from the second radiotracer are inherently less accurate than thesignals (i.e., counts) from the first radiotracer. This is because thesignals (i.e., counts) detected after the second radiotracer has beenadministered are due to both the remaining active portions of the firstradiotracer and the second radiotracer. It is important that there issufficient time gap between step 752, when the first radiotracer isadministered, and step 758, when the second radiotracer is administeredto the patient in order to make it easier to differentiate the effectsof the first radiotracer from those of the second radiotracer since thegamma photons detected by the PET detector are all of the same energy(511 keV). According to an exemplary embodiment, the clinician may waitapproximately 1 hour between administering the first radiotracer at step752 and administering the second radiotracer at step 758. It should beappreciated that at different amount of time between the administrationof the first radiotracer and the second radiotracer may be usedaccording to various embodiments.

The second radiotracer includes a different ligand than the firstradiotracer. As discussed above, the ligand used in a particularradiotracer determines the proteins with which the radiotracerinteracts. As such, imaging the patient with multiple differentradiotracers (each with a unique ligand) may provide different clinicianinsights regarding the functional activity of the patient.

After the second radiotracer has been administered at step 758, a secondCT dataset is acquired at step 760. A second PET dataset is acquired atstep 762. The second CT dataset may be a full CT dataset, or the secondCT dataset may be a low-dose CT dataset. The second CT dataset and thesecond PET dataset may be acquired using the same PET-CT hybrid imagingsystem that was used previously.

Next, at step 764, a processor, such as the processor 102 in theworkstation 100 accesses the first CT dataset, the first PET dataset,the second CT dataset, and the second PET dataset. According to anembodiment, the first CT dataset may be registered to the first PETdataset and the second CT dataset may be registered to the second PETdataset since the first PET dataset and the first CT dataset wereacquired on the PET-CT hybrid imaging system during a single session,and the second PET dataset and the second CT dataset were acquired onthe PET-CT hybrid imaging system during a single session. As is known bythose skilled in the art, the PET dataset and the CT dataset acquired ona hybrid imaging system are at a fixed spatial displacement from eachother, which is easy to determine based on the geometry of the hybridimaging system. The processor 102 may register the first CT dataset tothe first PET dataset and the second CT dataset to the second PETdataset. Or, according to other embodiments, the PET-CT hybrid imagingsystem may register the first CT dataset to the first PET dataset andthe second CT dataset to the second PET dataset.

At step 766, the processor 102 registers the first PET dataset to thesecond PET dataset. According to an exemplary embodiment, the processor102 may perform the registration by registering the first CT dataset tothe second CT dataset. Since the first PET dataset is registered to thefirst CT dataset and the second PET dataset is registered to the secondCT dataset, the processor 102 may register the first PET dataset to thesecond PET dataset by registering the first CT dataset to the second CTdataset. According to one embodiment, the processor 102 may determineany translations, rotations, and/or deformations necessary to registerthat first CT dataset to the second CT dataset and use the determinedtranslations, rotations, or deformations to register the first PETdataset to the second PET dataset. According to other embodiments thatdo not include anatomical imaging datasets, such as the first CT datasetor the second CT dataset, the processor 102 may directly register theplurality of functional imaging datasets to each other using theinformation in each respective functional imaging dataset.

FIG. 9 is a schematic representation of a portion of the method 750according to an embodiment. FIG. 9 includes a first block 802, a secondblock 804, a third block 806, and a fourth block 808. The first block802 represents a patient space. The volume-of-interest 502 is within thepatient space, and both the first PET dataset and the second PET datasetwere acquired from the volume-of-interest 502. The second block 804represent a patient image space. The patient image volume is within thepatient image space. For purposes of this disclosure, the plurality ofvoxels representing the volume-of-interest are considered as being inthe patient image space. According to the exemplary method representedin FIG. 9 , two functional imaging datasets were acquired: the first PETdataset; and the second PET dataset. Patient image volume 814 is aschematic representation of the voxel data from the first PET datasetacquired from within the volume-of-interest. In other words, each of thevoxels in the patient image volume 814 represents information acquiredduring step 756 (i.e., information from the first PET dataset)corresponding to a distinct sub-volume within the volume-of-interest.The third block 806 represents the patient image space. The patientimage volume is within the patient image space. For purposes of thisdisclosure, the plurality of voxels representing the volume-of-interestare considered as being in the patient image space. Patient image volume816 is a schematic representation of the voxel data from the second PETdataset acquired from within the volume-of-interest 502. In other words,each of the voxels in the patient image volume 816 representsinformation acquired during step 762 (i.e., information from the secondPET dataset) corresponding to a distinct sub-volume within thevolume-of-interest. Since the first PET dataset and the second PETdataset were both acquired from the same volume-of-interest, thoseskilled in the art should appreciate that there are two valuesassociated with each voxel location: a first voxel value correspondingto the first PET dataset; and a second voxel value corresponding to thesecond PET dataset.

Next, at step 768, the processor 102 determines a visualization priorityfor each pixel in a j oint-visualization image. The patient image volumeis a representation of the volume-of-interest in the patient imagespace. According to an embodiment, there are two different functionalimaging data values for each voxel within the patient image volume: afirst value corresponding to the first PET dataset and a second valuecorresponding to the second PET dataset. In other words, each voxelcorresponds to a specific sub-volume within the patient image volume.And two different functional imaging data values were acquired from eachsub-volume within the patient image volume. Therefore, there are twodifferent functional image data values for each voxel. According toother embodiments, which may include three or more functional imagingdata values for each voxel within the patient image volume, there wouldbe three or more functional imaging data values for each voxel withinthe patient image volume. Therefore, it may be necessary to determine avisualization priority for each of the voxels within the patient imagevolume. According to an embodiment, determining the visualizationpriority for each pixel may include individually determining, for eachvoxel in the patient image space, if the voxel should represent a firstvalue from the first PET dataset (i.e., representing a first functionalimaging data type) or a second value from the second PET dataset (i.e.,representing a second functional imaging data type). The processor 102may determine the visualization priority based on a comparison of theinformation from each of the plurality of functional imaging datasetsusing a set of logical rules stored in the memory 108.

According to an exemplary embodiment, the first PET dataset may havebeen acquired with ⁶⁸Ga-FAPI as the first radiotracer and the second PETdataset may have been acquired with ¹⁸F-FDG. It should be appreciatedthat ⁶⁸Ga-FAPI and ¹⁸F-FDG are merely exemplary radiotracers and thatthe method may use any other PET radiotracers including differentligands according to various embodiments. According to some embodiments,one or more of the functional imaging datasets may be a quantitativedataset including quantitative values in physical units or according tostandard calibrated scale. For example, the medical imaging system usedto acquire the functional imaging dataset may calibrate the acquireddata so that functional imaging dataset accessed by the processor 102 isa quantitative dataset. According to some embodiments where the acquireddata is not calibrated by the medical imaging system, the processor 102may be configured to convert the acquired data into quantitative dataincluding calibrated values. The processor 102 may convert one or moreof the functional imaging datasets according to a standard calibratedscale, such as the standardized uptake value (SUV) scale. For example,the processor 102 may first convert the first PET dataset and the secondPET dataset to a quantitative scale, such as the SUV scale. According toan exemplary embodiment, the processor 102 may convert the first PETdataset to a first SUV scale and the processor 102 may convert thesecond PET dataset to a second SUV scale. The first SUV scale may begenerated based on the first administered dose and the measurementsobtained when acquiring the first functional imaging dataset. Since thefirst radiotracer is the only radiotracer in the patient’s body at thetime when the first functional imaging dataset is being acquired, thecalculation of the first SUV scale is a relatively easy. Calculating thesecond SUV scale for the second radiotracer is not as straight-forwardsince the detected signals are due to both the first radiotracer, whichwas administered earlier, and the second radiotracer. The processor 102needs to calculate an estimate of the dose remaining from theadministration of the first radiotracer (⁶⁸Ga-FAPI), as gamma rays fromboth the first radiotracer and the second radiotracer (¹⁸F-FDG) will bedetected by the detector of the PET imaging system.

The processor 102 may implement an equation or algorithm to estimate theamount of contribution from first radiotracer (⁶⁸Ga-FAPI). The equationor algorithm may estimate the contribution based on factors, such as theamount of the first radiotracer (⁶⁸Ga-FAPI) administered to the patient,an estimated rate of accumulation of the first radiotracer in organs orlesions in the patient’s body, and the half-life of the firstradiotracer (⁶⁸Ga-FAPI). The processor 102 may access one or morelook-up tables stored in the memory 108 in order to obtain one or moreof the estimated rate of accumulation of the first radiotracer(⁶⁸Ga-FAPI) or the half-life of the first radiotracer (⁶⁸Ga-FAPI). Inorder to calculate the contribution from the second radiotracer(¹⁸F-FDG), the processor 102 may subtract the estimated contributionfrom the first radiotracer (⁶⁸Ga-FAPI) from the signals acquired by thedetector after the administration of the second radiotracer (¹⁸F-FDG).Since the contribution from the second radiotracer (¹⁸F-FDG) cannot bemeasured directly, and because the contributions from the firstradiotracer (⁶⁸Ga-FAPI) need to be estimated in order to calculate anestimated contribution of the second radiotracer (¹⁸F-FDG), themeasurements associated with the second functional imaging dataset andthe SUV scale for the second functional imaging dataset are inherentlyless accurate than the measurements associated with the first functionalimaging dataset and the SUV scale for the first functional imagingdataset. For these and other reasons, administering the most specific ofthe plurality of radiotracers to the patient first may be advantageoussince the signals obtained from the first radiotracer will always be themost accurate when dealing with clinical situations that ultimatelyinvolve having multiple PET radiotracers in the patient at the sametime. As discussed hereinabove, the SUV scale for each radiotracer maybe calculated independently according to an exemplary embodiment.

After converting the first functional imaging dataset and the secondfunctional imaging dataset to the SUV scale, the processor 102 maydetermine the visualization priority for each voxel based on the SUVvalues of the first and second functional imaging datasets. As discussedpreviously with respect to FIG. 9 , patient image volume 814 is aschematic representation of the voxel data from the first PET datasetacquired from within the volume-of-interest. Patient image volume 816 isa schematic representation of the voxel data from the second PET datasetacquired from within the volume-of-interest 502. There are two valuesassociated with each voxel location: the voxel value corresponding tothe first PET dataset; and the voxel value corresponding to the secondPET dataset.

According to an embodiment, determining the visualization priorityincludes individually determining, for each voxel in the patient imagevolume 818, if the voxel should represent the first PET value (i.e.,representing a first functional imaging data type) or the second PETvalue (i.e., representing a second functional imaging data type). Theprocessor 102 may be configured to determine the visualization priorityby applying logical rules stored in the memory 108. As discussedearlier, the logical rules may establish rules or thresholds todetermine which one of the functional imaging data types should berepresented by each of the plurality of voxels. According to anembodiment, the processor 102 may assign visualization priority to thefunctional imaging data type with the highest SUV value. For example,each voxel in the patient image space may represent information from thefunctional imaging dataset with the highest SUV value. As describedabove, there are two different functional imaging data values for eachvoxel within the patient image volume: a first PET value (from the firstPET dataset) and a second PET value (from the second PET dataset). Thefirst PET value may be converted to a first SUV value and the second PETvalue may be converted to a second SUV value. The processor 102 couldthen determine if the first SUV value or the second SUV is greater.According to an embodiment, the processor 102 would then assignvisualization priority to the functional imaging data type associatedwith the higher SUV value. For example, if, for a voxel, the first SUVvalue (⁶⁸Ga-FAPI) is higher than the second SUV value (¹⁸F-FDG), thenthat particular voxel would be assigned a visualization priority of thefirst functional imaging data type (associated with the first functionalimaging dataset). For another voxel, if the second SUV value (¹⁸F-FDG)is higher than the first SUV value (⁶⁸Ga-FAPI ), then the processor 102would assign a visualization priority of the second functional imagingdata type (associated with the second functional imaging dataset) forthat particular voxel. The processor 102 may proceed to individuallyassign visualization priorities to each of the voxel in the patientimaging space according to the method described hereinabove.

According to another embodiment, the processor 102 may be configured toapply a weighting factor to one or more of the functional imaging datatypes in order to emphasis one or more of the plurality of data types.For example, the logical rules stored in the memory 108 may include aweighting factor that is applied to one or more of the functionalimaging data types. For example, the logical rules may emphasize one ofthe functional imaging data types by one or both of applying a weightingfactor of greater than 1 (such as 1.25, 1.5, 1.75, etc.) to the SUVvalues from one of the functional imaging datasets to emphasize thatfunctional imaging data type associated with the one of the functionalimaging datasets; or applying a weighting factor of less than 1 (such as0.25, 0.5, 0.75, etc.) to another of the functional imaging data typesassociated with a different one of the functional imaging datasets. Forexample, to emphasize the first functional imaging data type, associatedwith the first functional imaging dataset (⁶⁸Ga-FAPI), the weightingfactor may include one or both of applying a weighting factor of greaterthan 1 to the SUV values generated from the first functional imagingdataset (⁶⁸Ga-FAPI) or applying a weighting factor of less than 1 to theSUV values generated form the second functional imaging dataset(¹⁸F-FDG). After applying the one or more weighting factors, weightedSUV data including weighted SUV values is created. According to anexemplary embodiment, the processor 102 may assign visualizationpriority to each of the plurality of voxels by selecting the functionalimaging data type associated with the functional imaging dataset withthe highest weighted SUV value. The embodiment described above wouldweight the first functional imaging data type associated with the firstfunctional imaging dataset (⁶⁸Ga-FAPI) more strongly than the secondfunctional imaging data type associated with the second functionalimaging dataset (¹⁸F-FDG). This may be desirable according to variousembodiments because ⁶⁸Ga-FAPI has a greater specificity as a radiotracerthan ¹⁸F-FDG. According to other embodiments, the logical rules may beconfigured to weigh one or more different functional imaging data types.For example, the second functional imaging data type, associated withthe second functional imaging dataset (¹⁸F-FDG) may be weighted morestrongly than the first functional imaging data type associated with thefirst functional imaging dataset (⁶⁸Ga-FAPI). In FIG. 9 , the patientimage volume 818 represents the plurality of voxels after thevisualization priority has been determined for each of the plurality ofvoxels. In other words, in the patient volume 818, each of the pluralityof voxels has a visualization priority that is associated with only oneof either the first functional imaging data type or the secondfunctional imaging data type. This means that each of the voxelsrepresents information from only one of the first functional imagingdataset or the second functional imaging dataset. The various functionalimaging data types may be weighted differently according to otherembodiments.

While the embodiment above described a technique for determiningvisualization priority based on SUV values, according to otherembodiments, the processor 102 may be configured to determine thevisualization priority for each of the plurality of voxels based onabsolute activity from the region within the patient’svolume-of-interest corresponding to each of the plurality of voxels. Forexample, the visualization priority for each of the plurality of voxelsin the patient image space may be determined by comparing the absoluteactivity level of the first functional imaging data type to the absoluteactivity level of the second functional imaging data type. As with theprevious embodiment, the logical rules stored in the memory 108 mayinclude a weighting factor that is applied to data from one or more ofthe functional imaging data types.

While the embodiments described above were specific to an embodimentwith two functional imaging data types (i.e., a first functional imagingdataset acquired with ⁶⁸Ga-FAPI and a second functional imaging datasetacquired with ¹⁸F-FDG), other embodiments may have three or moredifferent functional imaging data types. For example, additionalfunctional imaging datasets may be accessed by the processor 102.According to an embodiment, a unique radiotracer, including a ligandthat is unique from the ligands in the other radiotracers may beadministered to the patient before the acquisition of each additionalfunctional imaging dataset. Those skilled in the art will appreciatethat, for PET imaging, accurately differentiating the effects ofradiotracers administered to the patient while two or morepreviously-applied radiotracers are still in the patient’s body may beless accurate than embodiments using two or fewer distinct radiotracers.According to various embodiments, the processor 102 may use thevisualization priority that was determined for the voxels in the patientimage volume in order to determine the visualization priority for eachof the plurality of pixels in the joint-visualization image. This may beused, for example, when the joint-visualization image is a multiplanarreformation image. The processor may be configured to determine thevisualization priority for each of the plurality of pixels in thejoint-visualization image differently according to various embodiments.For example, other embodiments may not involve determining avisualization priority for each of the plurality of voxels in a patientimage volume. According to various other embodiments, the processor 102may be configured to determine the visualization priority for each ofthe plurality of pixels in the joint-visualization image based on alogical comparison of corresponding voxel data from correspondinglocations from two or more of the functional imaging datasets.Additional details will be described hereinafter.

Referring back to FIG. 8 , at step 770, the processor 102 generates ajoint-visualization image after determining the visualization priorityfor each of the plurality of pixels in the patient image volume 818.According to various embodiments, the processor 102 may generate thejoint-visualization image by generating a rendering based on the patientimage volume. FIG. 11 shows a representation of a joint-visualizationimage 850. The joint-visualization image 850 may be generated based on arendering using the voxels in the patient image volume 818 according toan embodiment. Next, at step 772, the processor 102 displays the joint-visualization image on the display device, such as the displaydevice 106. FIG. 11 includes a magnified portion 851. The magnifiedportion 851 includes a representation of a plurality of pixels 852 usedto display the j oint-visualization image 850. A subset of the pluralityof pixels 852 are shown schematically shown with cross-hatching in FIG.11 . Each pixel illustrated with cross-hatching represents the firstfunctional imaging data type (i.e., a value from the first functionalimaging dataset), and each pixels shown with hatching represents thesecond functional imaging data type (i.e., a value from the secondfunctional imaging dataset).

FIG. 10 illustrates a flowchart of an embodiment of a method 780. Thetechnical effect of the method 750 is the display of joint-visualizationimage on the display device 106. The joint-visualization imagerepresents information from a plurality of functional imaging data typesat the same time. Steps 794, 795, 796, 797, and 798 of the method 750may be performed with a workstation, such as the workstation 100 shownin FIG. 8 .

At step 782, a clinician administers a first radiotracer and a secondradiotracer to the patient. The first radiotracer includes a firstligand. The second radiotracer includes a second ligand that isdifferent than the first ligand. According to an exemplary embodiment,the first radiotracer may be iodine-123 and the second radiotracer maybe technetium-99m. At step 784, the clinician acquires a CT dataset ofthe patient. At step 786, the clinician acquires a first SPECT andsecond SPECT dataset from the volume of interest within the patient.Steps 784 and 786 may be performed with a SPECT-CT hybrid imagingsystem, for example. The clinician may administer the second radiotracerto the patient immediately after the first radiotracer since it ispossible to distinguish signals emitted from the first radiotracer fromsignals emitted from the second radiotracer when performing SPECTimaging. According to other embodiments, there may be a period of timebetween the administration of the first radiotracer and theadministration of the second radiotracer.

The second radiotracer includes a different ligand than the firstradiotracer. As discussed above, the ligand used in a particularradiotracer determines the proteins with which the radiotracerinteracts. As such, imaging the patient with multiple differentradiotracers (each with a unique ligand) may provide different clinicianinsights.

Next, at step 794, a processor, such as the processor 102 in theworkstation 100 accesses the first CT dataset, the first SPECT dataset,and the second SPECT dataset. The workstation may, for instance, accessthe datasets (i.e., the first CT dataset, the first SPECT dataset, andthe second SPECT dataset) from a memory or storage, from a Picturearchiving and communication system (PACS), from a local server, from aremote server, or directly from one or more imaging systems, such as theSPECT-CT imaging system. According to an embodiment, the first CTdataset may be registered to both the first SPECT dataset and the secondSPECT dataset since both SPECT datasets and the CT dataset were acquiredon the SPECT-CT hybrid imaging system during a single session. As isknown by those skilled in the art, the SPECT datasets and the CT datasetacquired on a hybrid imaging system are at a fixed spatial displacementfrom each other, which is easy to determine based on the geometry of thehybrid imaging system. According to other embodiments, the each of thetwo or more SPECT datasets may be acquired during a separateacquisition.

At step 795, the processor 102, registers the first SPECT dataset to thesecond SPECT dataset. According to an exemplary embodiment where thefirst SPECT dataset and the second SPECT dataset are acquired on thesame hybrid imaging system, the first and second SPECT datasets may bevery easy to register since they were both acquired with the same hybridsystem over the same period of time. According to other embodiments, thehybrid imaging system may register the first SPECT dataset to the secondSPECT dataset during or after the acquisition of the first and secondSPECT datasets.

Next, at step 796, the processor 102 determines a visualization priorityfor each pixel in a j oint-visualization image. The patient image volumeis a representation of the volume-of-interest in the patient imagespace. There are two different functional imaging data values for eachvoxel within the patient image volume: a first SPECT value from thefirst SPECT dataset and a second SPECT value from the second SPECTdataset. According to other embodiments, which may include three or morefunctional imaging data values for each voxel within the patient imagevolume, there would be three or more functional imaging data values foreach voxel within the patient image volume. Therefore, it may benecessary to determine a visualization priority for each of the voxelswithin the patient image volume. According to an embodiment, determiningthe visualization priority for each pixel may include individuallydetermining, for each voxel in the patient image space, if the voxelshould represent the first SPECT value (i.e., representing a firstfunctional imaging data type) or the second SPECT value (i.e.,representing a second functional imaging data type). The processor 102may then determine the visualization priority for each pixel based on alogical comparison of the information from each of the plurality offunctional imaging datasets using a set of logical rules stored in thememory 108. As discussed above, the processor 102 may identify avisualization priority for each of the plurality of pixels in thejoint-visualization image based on a logical comparison of correspondinginformation in each of the plurality of functional imaging datasets. Thevisualization priority determines which one of the plurality offunctional imaging datasets is represented by each of the plurality ofpixels in the joint-visualization image.

According to an exemplary embodiment, the first SPECT dataset may havebeen acquired with ¹²³I- sodium-iodide as the first radiotracer and thesecond SPECT dataset may have been acquired with ^(99m)Tc-tetrofosmin.According to an embodiment, ^(99m)Tc-tetrofosmin and ¹²³I- sodium-iodidecan be used simultaneously for the imaging of thyroid-related diseases,for example. In such cases, ¹²³I- sodium-iodide can have higherspecificity than the ^(99m)Tc-tetrofosmin, but the ^(99m)Tc-tetrofosmincan highlight additional relevant tissue/process types with lower iodineuptake. It should be appreciated that ¹²³I- sodium-iodide and^(99m)Tc-tetrofosmin are merely exemplary radiotracers and that themethod may use any other SPECT radiotracers with different ligandsaccording to various embodiments. According to some embodiments, one ormore of the functional imaging datasets may be a quantitative datasetincluding quantitative values in physical units or according to standardcalibrated scale. For example, the medical imaging system used toacquire the functional imaging dataset may calibrate the acquired dataso that functional imaging dataset accessed by the processor 102 is aquantitative dataset. According to some embodiments where the acquireddata is not calibrated by the medical imaging system, the processor 102may be configured to convert the acquired data into quantitative dataincluding calibrated values. For example, the processor 102 may convertone or more of the functional imaging datasets according to a standardcalibrated scale, such as the standardized uptake value (SUV) scale. Forexample, the processor 102 may first convert the first SPECT dataset andthe second SPECT dataset to a quantitative scale, such as the SUV scale,or any other quantitative scale. According to an exemplary embodiment,the processor 102 may convert the first SPECT dataset to a first SUVscale and the processor 102 may convert the second SPECT dataset to asecond SUV scale. The first SUV scale may be generated based on thefirst administered dose and the measurements obtained while acquiringthe first functional imaging dataset. The second SUV scale may begenerated based on the second administered dose and the measurementsobtained while acquiring the second functional imaging dataset.According to other embodiments, the first SPECT dataset and the secondSPECT dataset may be represented using an arbitrary scale. The arbitraryscale may, for instance, be determined by the image generation processand or imaging parameters.

In both PET and SPECT functional imaging, the functional imaging datawithin one or more of the functional imaging datasets may be provided inphysical activity concentration (MBq/mL) or in SUV. In some cases, thereconstructed images may be given in an arbitrary scale if all thecalibration parameters are not known. Functional MRI (fMRI) can showseveral different types of physiological processes. As in PET and SPECT,the image data may be in quantitative or non-quantitative scales. Forexample, in dynamic contrast-enhanced imaging with a Gadolinium agent,which may be used to analyze blood-in-tissue perfusion and permeability,image data may be provided in quantitative physical units of flow rateor related physical parameters. Similar to MRI, dynamiccontrast-enhanced imaging is a technique used in functional CT imagingwith Iodine contrast agent. In MRI, there are techniques for functionalimaging even without using Gadolinium contrast agent. The methodsdescribed herein may be used with either quantitative ornon-quantitative functional imaging datasets.

Referring back to FIG. 10 , after converting the first functionalimaging dataset and the second functional imaging dataset to the SUVscale, the processor 102 may determine the visualization priority foreach voxel based on the SUV values of the first and second functionalimaging datasets. As discussed previously, there are two valuesassociated with each voxel location: the voxel value corresponding tothe first SPECT dataset; and the voxel value corresponding to the secondSPECT dataset.

According to an embodiment, determining the visualization priority foreach pixel may include individually determining, for each voxel in thepatient image volume, if the voxel should represent the first SPECTvalue (i.e., representing a first functional imaging data type) or thesecond SPECT value (i.e., representing a second functional imaging datatype). The processor 102 may be configured to determine thevisualization priority by applying logical rules stored in the memory108. As discussed earlier, the logical rules may establish rules orthresholds to determine which one of the functional imaging data typesshould be represented by each of the plurality of voxels. According toan embodiment, the processor 102 may assign visualization priority tothe functional imaging data type with the highest SUV value. Forexample, each voxel in the patient image space may represent informationfrom the functional imaging dataset with the highest SUV value. Asdescribed above, there are two different functional imaging data valuesfor each voxel within the patient image volume: a first SPECT value anda second SPECT value. The first SPECT value is converted to a first SUVvalue and the second SPECT value is converted to a second SUV value. Theprocessor could then determine if the first SUV value or the second SUVis greater. According to an embodiment, the processor 102 would thenassign visualization priority to the functional imaging data typeassociated with the higher SUV value. For example, if, for a voxel, thefirst SUV value (¹²³I- sodium-iodide ) is higher than the second SUVvalue (^(99m)Tc-tetrofosmin), then that particular voxel would beassigned a visualization priority of the first functional imaging datatype (associated with the first functional imaging dataset). For anothervoxel, if the second SUV value (^(99m)Tc-tetrofosmin) is higher than thefirst SUV value (¹²³I- sodium-iodide ), then the processor 102 wouldassign a visualization priority of the second functional imaging datatype (associated with the second functional imaging dataset). Theprocessor 102 may proceed to individually assign visualizationpriorities to each of the voxel in the patient imaging space accordingto the method described hereinabove.

According to another embodiment, the processor 102 may be configured toapply a weighting factor to one or more of the functional imaging datatypes in order to emphasis one or more of the plurality of data types.For example, the logical rules stored in the memory 108 may include aweighting factor that is applied to one or more of the functionalimaging data types. For example, the logical rules may emphasize one ofthe functional imaging data types by one or both of applying a weightingfactor of greater than 1 (such as 1.25, 1.5, 1.75, etc.) to the SUVvalues from one of the functional imaging datasets to emphasize thatfunctional imaging data type associated with the one of the functionalimaging datasets; or applying a weighting factor of less than 1 (such as0.25, 0.5, 0.75, etc.) to another of the functional imaging data typesassociated with a different one of the functional imaging datasets. Forexample, to emphasize the first functional imaging data type, associatedwith the first functional imaging dataset (¹²³I-sodium-iodide ), theweighting factor may include one or both of applying a weighting factorof greater than 1 to the SUV values generated from the first functionalimaging dataset (¹²³I- sodium-iodide ) or applying a weighting factor ofless than 1 to the SUV values generated form the second functionalimaging dataset (^(99m)Tc-tetrofosmin). After applying the one or moreweighting factors, weighted SUV data, including weighted SUV values, iscreated. According to an exemplary embodiment, the processor 102 mayassign visualization priority to each of the plurality of voxels byselecting the functional imaging data type associated with thefunctional imaging dataset with the highest weighted SUV value. Theembodiment described above would weight the first functional imagingdata type associated with the first functional imaging dataset(¹²³I-sodium-iodide ) more strongly than the second functional imagingdata type associated with the second functional imaging dataset(^(99m)Tc-tetrofosmin). This may be desirable according to variousembodiments because ¹²³I- sodium-iodide may have a greater specificityas a radiotracer than ^(99m)Tc-tetrofosmin for certain clinicalapplications, such as the imaging of thyroid-related diseases. Accordingto other embodiments, the logical rules may be configured to weight oneor more different functional imaging data types. For example, the secondfunctional imaging data type, associated with the second functionalimaging dataset (^(99m)Tc-tetrofosmin) may be weighted more stronglythan the first functional imaging data type associated with the firstfunctional imaging dataset (¹²³I- sodium-iodide).

While the embodiment above described a technique for determiningvisualization priority based on SUV values, according to otherembodiments, the processor 102 may be configured to determine thevisualization priority for each of the plurality of voxels based onabsolute activity from the region within the patient’svolume-of-interest corresponding to each of the plurality of voxels. Forexample, the visualization priority for each of the plurality of voxelsin the patient image space may be determined by comparing the absoluteactivity level of the first functional imaging data type to the absoluteactivity level of the second functional imaging data type. As with theprevious embodiment, the logical rules stored in the memory 108 mayinclude a weighting factor that is applied to one or more of thefunctional imaging data types. The processor may be configured todetermine the visualization priority for each of the plurality of pixelsin the joint-visualization image differently according to variousembodiments. For example, other embodiments may not involve determininga visualization priority for each of the plurality of voxels in apatient image volume. According to various other embodiments, theprocessor 102 may be configured to determine the visualization prioritybased on a logical comparison of corresponding voxel data from two ormore of the functional imaging datasets. Additional details will bedescribed hereinafter.

While the embodiments described above were specific to an embodimentwith two functional imaging data types (i.e., a first functional imagingdataset and a second functional imaging dataset), other embodiments mayhave three or more different functional imaging data types. For example,additional functional imaging datasets may be acquired and accessed bythe processor 102. According to an embodiment, a unique radiotracer,including a ligand that is unique from the ligands in the otherradiotracers, may be administered to the patient before the acquisitionof each additional functional imaging dataset. The additionalradiotracer/s may be administered to the patient at generally the sametime as the first radiotracer, or according to other embodiments, theymay be spaced out by a number of minutes.

Referring back to FIG. 10 , at step 797, the processor 102 generates ajoint-visualization image after determining the visualization priorityfor each of the plurality of voxels in the patient image volume.According to many embodiments, the processor 102 may generate thejoint-visualization image by generating a rendering based on the patientimage volume. FIG. 11 shows a representation of a joint-visualizationimage 850. The joint-visualization image 850 may be generated based on arendering using the voxels in the patient image volume according to anembodiment. Next, at step 798, the processor 102 displays thejoint-visualization image on the display device, such as the displaydevice 106.

FIG. 12 shows a representation of a side-view of the patient imagevolume 818 with respect to the portion of the joint-visualization image850. FIG. 13 shows a representation of a perspective view of a singleray through the patient image volume 818 with respect to a portion ofthe joint-visualization image 851. As shown in FIG. 11 , the portion ofthe joint-visualization image 851 is a sub-region of thejoint-visualization image 850. FIG. 12 will be used to explain how thejoint-visualization image 850 may be generated in accordance withvarious embodiments. As described above, after determining thevisualization priority, each of the plurality of voxels in the patientimage volume 818 represents information from a single one of theplurality of functional imaging datasets. FIG. 12 shows a ray 860passing through the patient image volume 818. The perspective of thepatient image volume 818 is from the side view, so only a single layersof voxels in the patient image volume 818 is visible in FIG. 12 . Theray 860 passes intersects a plurality of voxels within the patient imagevolume 818. The ray 860 shown in FIG. 12 is parallel to some of thesurfaces of the voxels, but it should be appreciated that the rays usedto generate the joint-visualization image 850 may pass through thepatient image volume 818 at any arbitrary angle according to variousembodiments. The ultimate value of the pixel in the joint visualizationimage 850 may be generated based on a ray-casting technique, such asthat schematically represented by FIG. 12 . According to otherembodiments, the values of some of the pixels in the joint-visualizationimage 850 may be generated by ray-casting, while the values of otherpixels may be generated by interpolating between pixel values generatedby ray-casting.

The processor 102, may generate a multiplanar reformation image. FIG. 13shows a schematic representation of the patient image volume 818 and animage plane 900. The multiplanar reformation image is two-dimensionalimage of an arbitrary plane that is reconstructed from voxel data in thepatient image volume. The image plane 900 in FIGS. 14 represents anexemplary image plane. The multiplanar reformation image of the imageplane 900 is a visual representation of the voxel data intersected bythe image plane 900. According to one embodiment, the processor 102 maybe configured to apply multiple color-mapping schemes to the voxel dataintersected by the image plane 900 after the selection of the imageplane. According to other embodiments, the processor 102 may firstgenerate a volume-rendering of the patient image volume 818 and may thengenerate the multiplanar reformation image based on thevolume-rendering. A first of the plurality of pixels in the joint-visualization image 850 may be displayed using a firstcolor-mapping scheme corresponding to the first functional imaging datatype (from the first functional imaging dataset); and a second pluralityof pixels in the joint-visualization image 850 may be displayed using asecond color-mapping scheme corresponding to the second functionalimaging data type (from the second functional imaging dataset). This waythe clinician may quickly and easily distinguish the pixels associatedwith the first functional imaging dataset from the pixels associatedwith the second functional imaging dataset.

According to an exemplary embodiment wherein both the first and secondfunctional imaging datasets are registered to a first anatomical imagingdataset and a second anatomical imaging dataset, respectively,registering the first functional imaging dataset to the secondfunctional imaging dataset may be performed by registering the firstanatomical imaging dataset to the second anatomical imaging dataset.

While the joint-visualization image may be displayed alone, in manyapplications, the joint-visualization image will be displayed as anoverlay on an anatomical image in a fusion mode. Referring to the method750 shown in FIG. 8 , the anatomical image may be generated from thefirst CT dataset, which was a full-dose CT dataset. According to otherembodiments, the anatomical image may be generated from the second CTdataset. The anatomical image generated from the CT dataset includesanatomical details of a much higher resolution than those shown in thejoint-visualization image, based on portions of the first and second PETdatasets. As discussed previously, the functional imaging datasets (i.e.the PET datasets) may be registered to anatomical imaging dataset (i.e.,the CT dataset) when the data is acquired with a hybrid PET-CT imagingsystem. It is therefore relatively easy to register thejoint-visualization image (generated from the PET datasets) to theanatomical image (generated from the first CT dataset).

As mentioned previously, determining the visualization priority for eachof the plurality of pixels in the joint-visualization image may notinvolve determining a visualization priority for each of the pluralityof voxels according to various embodiments. For example, the processor102 may be configured to determine the visualization priority based on alogical comparison of corresponding voxel data from two or more of thefunctional imaging datasets. This may be used, for instance, when thejoint-visualization image is a MIP image or a MinIP image. FIG. 14A andFIG. 14B will be used to explain an exemplary embodiment.

FIG. 14A shows a schematic representation of the ray 860 passing througha plurality of voxels associated with a first functional imaging datasetand FIG. 14B shows a schematic representation of the ray 860 passingthrough a plurality of voxels associated with a second functionalimaging dataset. Only 10 voxels are schematically represented in FIG.14A, but it should be appreciated that ray-casting would typicallyinvolve casting a ray through a significantly larger number of voxelsaccording to most embodiments. FIG. 14A includes a first voxel 854, asecond voxel 856, a third voxel 858, a fourth voxel 859, a fifth voxel862, a sixth voxel 864, a seventh voxel 866, an eighth voxel 868, aninth voxel 870, and a tenth voxel 872. As discussed previously, each ofthe voxels (854, 856, 858, 859, 862, 864, 866, 868, 870, and 872)represents information from only one of the plurality of functionalimaging data types. According to an embodiment, each of the voxels (854,856, 858, 859, 862, 864, 866, 868, 870, and 872) represents the firstfunctional imaging data type (from the first functional imagingdataset).

According to an embodiment where the joint-visualization image is amaximum-intensity-projection (MIP) image, each pixel on thejoint-visualization image 850 represents that maximum pixel value alongthe ray. So, the pixel 852 would represent the value of the maximumvoxel along the ray 860. Assuming that the seventh voxel 866 has thehighest value of the voxels (854, 856, 858, 859, 862, 864, 866, 868,870, and 872), then the pixel 852 would represent the information invoxel 866.

Similarly, for a minimum-intensity-projection (MinIP) image, theprocessor 102 may identify the voxel along each ray, such as the ray860, with the minimum value. The processor 102 would then assign thevalue and functional imaging data type of the voxel with the minimumvalue to the associated pixel, such as the pixel 852. Assuming that thefirst voxel 854 had the minimum value of all the voxels along the ray860, the pixel 852 would be assigned the value of the first voxel.

For embodiments where the joint-visualization image is either a MIPimage or a MinIP image, a MIP or MinIP value from each of the functionalimaging datasets is associated with each of the pixels. For this reason,it is necessary to determine a visualization priority for each of theplurality of pixels to define which one of the plurality of functionalimaging data types is represented by each pixel in thejoint-visualization image. As discussed previously, the processor 102may determine a visualization priority for each pixel in thejoint-visualization image by performing a logical comparison ofcorresponding information in each of the plurality of functional imagingdatasets. For embodiments where the j oint-visualization image is amaximum-intensity-projection (MIP) image, the processor 102 may comparea first maximum-intensity projection (MIP) value calculated from a firstfunctional imaging dataset with a second maximum-intensity-projection(MIP) value calculated from a second functional imaging dataset. Forembodiments where the joint-visualization image is aminimum-intensity-projection (MinIP) image, the processor 102 maycompare a first minimum-intensity projection (MinIP) value calculatedfrom a first of the plurality of datasets with a secondminimum-intensity-projection (MinIP) value calculated from a second ofthe plurality of functional imaging datasets.

As discussed previously, FIG. 14A shows a representation of aperspective view of a single ray through a patient image volume withrespect to a portion of the joint-visualization image 851. Forembodiments where the joint-visualization image 851 is a MIP image, theprocessor 102 may calculate a first maximum-intensity-projection (MIP)value from the first functional imaging dataset and compare it to asecond maximum-intensity-projection (MIP) values from the secondfunctional imaging dataset. As discussed above, the processor 102 may beconfigured to determine the visualization priority for each pixel in thej oint-visualization image based on a logical comparison ofcorresponding information in each of the plurality of functional imagingdatasets. According to an embodiment, the first functional imagingdataset may represent a first functional imaging data type and a secondfunctional imaging dataset may represent a second functional imagingdata type. In order to determine the visualization priority for pixel852, the processor 102 may first identify a firstmaximum-intensity-projection value from the first functional imagingdataset and a second maximum-intensity-projection value from the secondfunctional imaging dataset. The processor 102 may then perform a logicalcomparison of the first maximum-intensity-projection value from thefirst functional imaging dataset with the secondmaximum-intensity-projection value from the second functional imagingdataset using one or more logical rules. FIG. 14A shows ray 860 withrespect to voxels from the first functional imaging dataset. Thecorresponding information from the second functional imaging dataset isfrom a corresponding sub-volume within the volume-of-interest. FIG. 14Bshow ray 860 with respect to voxels from the second functional imagingdataset. Voxels 874, 876, 878, 880, 882, 884, 886, 888, 890, and 892represents information acquired from the same sub-volumes of the patientas voxels 854, 856, 858, 859, 862, 864, 866, 868, 870, and 872 (shown inFIG. 14A) The voxels used to calculate the first MIP value correspond tothe same sub-volume in the patient as the voxels used to calculate thesecond MIP value. For example, voxels 854, 856, 858, 859, 862, 864, 866,868, 870, and 872 may represent information from the first functionalimaging dataset and the MIP value may therefore represent the first MIPvalue. A second MIP value would be calculated from voxels 874, 876, 878,880, 882, 884, 886, 888, 890, and 892 in the second functional imagingdataset. FIG. 14A represents how a first MIP value (from the firstfunctional imaging dataset) may be calculated for pixel 852 and FIG. 14Brepresents how a second MIP value (from the second functional imagingdataset) may be calculated for pixel 852.

The processor 102 may then perform a logical comparison of the first MIPvalue and the second MIP value in order to determine a visualizationpriority for pixel 852. The processor 102 may determine thevisualization priority for each pixel based on the SUV values of thefirst and second functional imaging datasets. For example, each of thevoxel values may represent an SUV value. The processor 102 may beconfigured to determine the visualization priority by applying logicalrules stored in the memory 108. As discussed earlier, the logical rulesmay establish rules or thresholds to determine which one of thefunctional imaging data types should be represented by each of theplurality of pixels. According to an embodiment, the processor 102 mayassign visualization priority to the functional imaging data type withthe highest SUV value. For example, each pixel in thejoint-visualization image may represent information from the functionalimaging dataset with the highest SUV value. For example, with respect tothe pixel 852, if the MIP value associated with the first functionalimaging data type is higher than MIP value associated with the secondfunctional imaging data type then that particular voxel would beassigned a visualization priority of the first functional imaging datatype (associated with the first functional imaging dataset). On theother hand, for pixel 852, if MIP value associated with the secondfunctional imaging data type is higher than MIP value associated withthe first functional imaging data type, then the processor 102 wouldassign a visualization priority of the second functional imaging datatype (associated with the second functional imaging dataset) to pixel852. The processor 102 may proceed to individually assign visualizationpriorities to each of the pixels in the joint-visualization imageaccording to the method described hereinabove.

According to another embodiment, the processor 102 may be configured toapply a weighting factor to one or more of the functional imaging datatypes in order to emphasis one or more of the plurality of data types.For example, the logical rules stored in the memory 108 may include aweighting factor that is applied to one or more of the functionalimaging data types. For example, the logical rules may emphasize one ofthe functional imaging data types by one or both of applying a weightingfactor of greater than 1 (such as 1.25, 1.5, 1.75, etc.) to the SUVvalues from one of the functional imaging datasets to emphasize thatfunctional imaging data type associated with the one of the functionalimaging datasets; or applying a weighting factor of less than 1 (such as0.25, 0.5, 0.75, etc.) to another of the functional imaging data typesassociated with a different one of the functional imaging datasets. Forexample, to emphasize the first functional imaging data type, associatedwith the first functional imaging dataset (¹²³I-sodium-iodide ), theweighting factor may include one or both of applying a weighting factorof greater than 1 to the SUV values generated from the first functionalimaging dataset or applying a weighting factor of less than 1 to the SUVvalues generated form the second functional imaging dataset. Accordingto other embodiments, various weighting factors may be applied to a MIPvalue or a MinIP value calculated from the each of the functionalimaging datasets in order to more-strongly emphasis one of thefunctional imaging data types. After applying the one or more weightingfactors, weighted SUV data, including weighted SUV values, is created.According to an exemplary embodiment, the processor 102 may assignvisualization priority to each of the plurality of pixels by selectingthe functional imaging data type associated with the functional imagingdataset with the highest weighted SUV value. The embodiment describedabove would weight the first functional imaging data type associatedwith the first functional imaging dataset more strongly than the secondfunctional imaging data type associated with the second functionalimaging dataset. According to other embodiments, the logical rules maybe configured to weight one or more different functional imaging datatypes. For example, the second functional imaging data type, associatedwith the second functional imaging dataset may be weighted more stronglythan the first functional imaging data type associated with the firstfunctional imaging dataset.

While the embodiment above described a technique for determiningvisualization priority based on SUV values, according to otherembodiments, the processor 102 may be configured to determine thevisualization priority for each of the plurality of pixels based onabsolute activity from the region within the patient’svolume-of-interest corresponding to each of the plurality of pixels. Forexample, the visualization priority for each of the plurality of pixelsin the joint-visualization image may be determined by comparing theabsolute activity level of the first functional imaging data type to theabsolute activity level of the second functional imaging data type. Aswith the previous embodiment, the logical rules stored in the memory 108may include a weighting factor that is applied to one or more of thefunctional imaging data types. For embodiments where the joint-visualization image is a MinIP image, the processor 102 may beconfigured to determine the visualization priority for each of thepixels by comparing a first minimum-intensity-projection (MinIP) valuecalculated from along a ray through a first functional imaging datasetwith a second minimum-intensity-projection (MinIP) value calculatedalong a ray with a corresponding position through the second functionalimaging dataset. The processor 102 may, for each pixel in thejoint-visualization image, select the functional imaging data type fromthe functional imaging dataset with the lower MinIP value. According toother embodiments, the processor 102 may be configured to determine thevisualization priority for each pixel in the joint-visualization imagebased on any number of logical rules comparing corresponding voxelvalues between two or more different functional imaging datasets.

While the embodiments described above were specific to an embodimentwith two functional imaging data types, other embodiments may have threeor more different functional imaging data types. For example, additionalfunctional imaging datasets may be acquired and accessed by theprocessor 102. According to an embodiment, a unique radiotracer,including a ligand that is unique from the ligands in the otherradiotracers, may be administered to the patient before the acquisitionof each additional functional imaging dataset. The additionalradiotracer/s may be administered to the patient at generally the sametime as the first radiotracer, or according to other embodiments, theymay be spaced out by a number of minutes.

Each pixel in the joint-visualization image 850 represents only a singleone of the plurality of functional imaging data types. For example, inthe embodiment discussed with respect to FIG. 8 , each pixel representseither the first functional imaging data type (from the first functionalimaging dataset acquired using ⁶⁸Ga-FAPI) or the second functionalimaging data type (from the second functional imaging dataset acquiredusing ¹⁸F-FDG). For the pixels in the joint-visualization image, a firstcolor-mapping scheme may be used to represent pixels associated with afirst of the plurality of functional imaging datasets and a second,different, color-mapping scheme may be used to represent pixelsassociated with a second of the plurality of functional imagingdatasets. Regarding the embodiment described with respect to FIG. 8 , afirst of the plurality of pixels in the joint-visualization image 850may be displayed using a first color-mapping scheme corresponding to thefirst functional imaging data type (from the first functional imagingdataset acquired using ⁶⁸Ga-FAPI) and a second plurality of pixels inthe joint-visualization image 850 may be displayed using a secondcolor-mapping scheme corresponding to the second functional imaging datatype (from the second functional imaging dataset acquired using¹⁸F-FDG). The visualization priority for each of the plurality of pixelsin the joint-visualization image may be determined according to themethods described hereinabove with respect to MIP and MinIP images inany of the previously described embodiments. For example, one or more ofthese techniques may be used at step 306 of the method 300, at step 768of the method 750, or at step 796 of the method 780. Additionally, oneor more of these techniques may be used at step 988 of the method 980(shown in FIG. 16 ), which has not been described in detail yet.

The processor 102 may, for example, access one or more look-up tablesstored in the memory 108 in order to determine how to represent eachpixel. There may, for instance, be a separate color-mapping schemestored in each look-up table. According to an exemplary embodiment, theprocessor 102 may access a first look-up table for a first color-mappingscheme in order to generate the colors and values for the firstplurality of pixels associated with the first functional imaging datatype and the processor 102 may access a second look-up table for asecond color-mapping scheme in order to generate the colors and valuesfor the second plurality of pixels associated with the second functionalimaging data type. The first color-mapping scheme is different than thesecond color-mapping scheme. According to other embodiments, the firstcolor-scheme may include a range of colors. For example, the firstcolor-mapping scheme may include colors that fall within the hues ofred, orange, and yellow; and the second color-mapping scheme may includecolors that fall within the hues of green, blue, and purple. It shouldbe appreciated that each color-mapping scheme may include a differentrange of colors according to various embodiments. According to anembodiment, the first color-mapping scheme may be primarily a firstcolor and the second color-mapping scheme may be primarily a secondcolor that is different than the first color. According to anembodiment, the first color may be red, and the second color may beblue, but each color-mapping schemes may include any one or more colorsaccording to various embodiments. Each color-mapping scheme used togenerate the joint-visualization image may map the same value to adifferent color. For example, the first color-mapping scheme may map afirst value to a first color while a second color-mapping scheme may mapthe same first value to a second color that his different than the firstcolor. According to an embodiment, on the joint-visualization image, allthe pixels representing the first functional imaging data type (from thefirst functional imaging dataset) may be represented primarily in thefirst color and all the pixels representing the second functionalimaging data type (from the second functional imaging dataset) may berepresented primarily in the second color. This way, the clinician willbe able to quickly and easily tell, for each pixel, if the data on thejoint-functional image is from the first functional imaging dataset orthe second functional imaging dataset based on the color-scheme used todisplay each respective pixel.

FIG. 15 is a schematic illustration of fusion image in accordance withan exemplary embodiment. FIG. 15 includes a joint-visualization image970, an anatomical image 972, and a fusion image 974. Thejoint-visualization image 970 may include information from any two ormore different functional imaging data types. The joint-visualizationimage may include information acquired with two or more different PETradiotracers, information acquired using two or more different SPECTradiotracers, information acquired using one or more PET radiotracersand one or more SPECT radiotracers, or information acquired with an fMRIimaging system and information acquired with one or more PET tracersand/or information acquired with one or more SPET tracers.

The anatomical image may be generated from an anatomical imaging datasetsuch as an CT anatomical imaging dataset, an MRI anatomical imagingdataset or an ultrasound anatomical imaging dataset. For embodimentsusing a hybrid imaging system, such as a PET-CT imaging system, aSPECT-CT imaging system, an MRI-PET imaging system or an MRI-SPECTimaging system, it is anticipated that the anatomical imaging datasetwill be either a CT anatomical imaging dataset or an MRI anatomicalimaging dataset, respectively.

The fusion image 974 is generated by overlaying the joint-visualizationimage 970 on the anatomical image 972. According to an embodiment, theanatomical image may be represented in greyscale and thejoint-visualization image may be represented using one or more differentcolors. According to various embodiments one or both of thejoint-visualization image 970 and the anatomical image 972 may bedisplayed in a semi-transparent manner in the fusion image 974 so thatthe user may see information from both anatomical image and thejoint-visualization image in the fusion image 974. According to variousembodiments, the clinician may adjust the transparency levels of one orboth of the joint-visualization image 970 and the anatomical image 972in the fusion image 974 in order to adjust the relative impact of thetwo images (i.e., the joint-visualization image and the anatomicalimage) in the fusion image 974. According to some embodiment theclinician may adjust the transparency levels of the joint-visualizationimage 970 and the anatomical image 972 in the fusion image 974 via theuser interface by adjusting one or two slide bars or one or two virtualslide bars.

According to various embodiments, the clinician may be able to adjustone or more of the color-mapping schemes used to generate thejoint-visualization image. FIG. 16 is a flow chart of a method 980 whichwill be used to describe an exemplary embodiment where one or more colormapping schemes are adjusted. The method 980 shown in FIG. 16 may beperformed with a workstation, such as the workstations 100 shown in FIG.1 . The technical effect of the method 980 is the display ofjoint-visualization image on the display device 106. Thejoint-visualization image 970 represents information from a plurality offunctional imaging data types at the same time.

At step 982, the processor 102 accesses a first functional imagingdataset. At step 984, the processor 102 accesses a second functionalimaging dataset. At step 986, the processor registers the firstfunctional imaging dataset to the second functional imaging dataset. Atstep 988, the processor 102 determines a visualization priority for eachpixel in a joint-visualization image based on a logical comparison ofcorresponding information in each of the plurality of functional imagingdatasets. The processor 102 may determine the visualization priorityusing, for example, any of the previously-discussed techniques. Next, atstep 990, the processor 102 accesses a color-mapping scheme for each ofthe functional imaging datasets. Since the embodiment 980 includes afirst functional imaging dataset and a second functional imagingdataset, the processor 102 may access a first color-mapping scheme torepresent the first of the functional imaging datasets and a secondcolor-mapping scheme to represent the second of the functional imagingdatasets. The first color-mapping scheme is different than the secondcolor-mapping scheme. At step 992, the processor 102 generates ajoint-visualization image and at step 994, the processor displays thejoint-visualization imaging on a display device. Steps 982, 984, 986,988, 990, 992, and 994 correspond with steps that were previouslydescribed with respect to the method 300, shown in FIG. 3 , and themethod 750, shown in FIG. 8 , and, as such, will not be described inadditional detail.

At step 996 of the method 980, the processor 102 may receive in inputfrom the user interface 104 adjusting a color-mapping scheme. If aninput is not received at step 996, the method 980 may advance to step998 where it ends. However, if an input is received from the userinterface, the method 980 returns to step 992, and steps 992 and 994 areperformed again. The input received at step 996 from the user interface104 may adjust one or more of the color-mapping schemes used to generatethe joint-visualization image. The processor 102 may receive anadjustment to the first color-mapping scheme and/or the secondcolor-mapping scheme according to various embodiments. At step 992, theprocessor 102 generates an updated joint-visualization image using theadjusted color-mapping scheme/s. The updated joint-visualization imagemay be displayed on the display device in place of the previouslydisplayed joint visualization image. The method 980 may iterativelyrepeat steps 992, 994, and 996 as many times as it is desired to adjustone or more of the color-mapping schemes. This allows the clinician toadjust the colors used in each color-mapping scheme in order to helpvisualize one or more regions on the joint-visualization image moreclearly. As described with respect to previous embodiments, thejoint-visualization image may be displayed as an overlay on ananatomical image in a fusion mode according to various embodiments.

The functional imaging data may be rescaled for any of the embodimentsdescribed herein. For example, a useful baseline reference can be thecalculated typical activity of normal tissues in the specific scannedpatient. This can be calculated by, for example, first segmenting theoverall soft-tissues using the anatomical CT image data (from the PET-CTprotocol) and then calculating the median activity of these voxels (onthe PET image data). Based on this process, a visualization prioritythreshold can be set, for example, as image values larger than thebaseline value or larger than twice of the baseline value, etc. Theprinciple of re-scaling the image data relative to the normal tissuevalue, can be applied for MRI or any other functional imaging modalityaccording to various embodiments.

There are numerous advantages provided by various embodiments of theinvention. For example, embodiments provide a method for displaying ajoint-visualization image representing information from multipledifferent functional imaging data types. For example, the multipledifferent functional imaging data types may have been acquired using twoor more different radiotracers. Embodiments of the invention involvedisplaying information from each of the different functional imagingdatasets with a unique quantitative scale. For example, information froma first functional imaging dataset may be displayed with a firstquantitative scale while information from a second functional imagingdataset may be displayed with a second, different, quantitative scale.The quantitative scale used to represent the information in eachparticular functional imaging dataset may be unique to that particularfunctional imaging dataset. Since it is not necessary to display valuesfrom multiple functional imaging datasets on the same quantitativescale, it is possible to optimize each quantitative scale based on thedata within that particular functional imaging dataset. This allows thequantitative scale used for each functional imaging dataset be a betterfit to the range of data values within that particular functionalimaging dataset. This, in turn, enables the processor 102 tomore-accurately display the relevant differences within a particularfunctional imaging dataset. For example, if all of the value for aparticular functional imaging dataset are compressed within a smallrange of values, it may be desirable to use a different quantitativescale than for a dataset with data values that extend over a much largerrange of values. According to various embodiments, one or more of thequantitative scales may be adjusted either manually or based on aparticular clinical application. Additionally, as discussed previously,embodiments may further involve displaying the joint-visualization imageas an overlay on an anatomical image in a fusion mode. This providesclinician with information from two or more different functional imagingdata types at the same time as an anatomical image in order to providethe clinician with additional anatomical context when viewing andinterpreting the image. Various embodiments provide the clinician withan easy and intuitive way to view and interact with multiple differenttypes of data (i.e., multiple different anatomical imaging data typesand anatomical imaging data). This can help save time and results in afaster and easier way to make a diagnosis for certain clinicalsituations. Additionally, various embodiments of the invention involvedetermining a visualization priority for each of a plurality of pixelsin the joint-visualization image. This means that each pixel representsinformation from only a single one of the functional imaging data types.This is an improvement over conventional solutions which sometimes tryto represent multiple different types of functional imaging data in asingle pixel. With these conventional solutions, it is difficult orimpossible to determine if the pixel value in the displayed image isbased on a first type of functional imaging data or a second type offunctional imaging data. The invention makes a significant improvementbecause each pixel in the joint-visualization image represents only asingle functional imaging data type, which makes the interpretation ofthe joint-visualization image easier.

The techniques presented and claimed herein are referenced and appliedto material objects and concrete examples of a practical nature thatdemonstrably improve the present technical field and, as such, are notabstract, intangible or purely theoretical.

This written description uses examples to disclose the subject matter,including the best mode, and also to enable any person skilled in theart to practice the subject matter, including making and using anydevices or systems and performing any incorporated methods. Thepatentable scope of the subject matter is defined by the claims, and mayinclude other examples that occur to those skilled in the art. Suchother examples are intended to be within the scope of the claims if theyhave structural elements that do not differ from the literal language ofthe claims, or if they include equivalent structural elements withinsubstantial differences from the literal languages of the claims.

1. A method of functional imaging comprising: accessing a plurality offunctional imaging datasets acquired from a volume-of-interest, whereineach of the plurality of functional imaging datasets is a different oneof a plurality of functional imaging data types; registering theplurality of functional imaging datasets to each other; determining avisualization priority for each of a plurality of pixels in ajoint-visualization image based on a logical comparison of correspondinginformation in each of the plurality of functional imaging dataset,wherein the visualization priority defines which one of the plurality offunctional imaging data types will be represented by each of theplurality of pixels; generating the joint-visualization image based onthe visualization priority determined for each of the plurality ofpixels and at least a portion of each of the plurality of functionalimaging datasets, wherein the joint-visualization image representsinformation from each of the plurality of functional imaging data typesat the same time, and wherein each of the plurality of pixels in thejoint-visualization image represents only a single one of the pluralityof functional imaging data types; and displaying the joint-visualizationimage on a display device.
 2. The method of claim 1, wherein thejoint-visualization image is selected from the list consisting of amaximum-intensity-projection (MIP) image, a minimum-intensity-projection(MinIP) image, and a multiplanar reformation image.
 3. The method ofclaim 1, wherein the joint-visualization image comprises one of amaximum-intensity-projection (MIP) image or aminimum-intensity-projection (MinIP) image, and wherein said performingthe logical comparison of corresponding information comprises either:comparing a first maximum-intensity-projection (MIP) value calculatedfrom a first of the plurality of functional imaging datasets with asecond maximum-intensity-projection (MIP) value calculated from a secondof the plurality of functional imaging datasets when thejoint-visualization image is a maximum-intensity projection (MIP) image;or comparing a first minimum-intensity-projection (MinIP) valuecalculated from the first of the plurality of functional imagingdatasets with a second minimum-intensity-projection (MinIP) valuecalculated from the second of the plurality of functional imagingdatasets when the joint-visualization image is aminimum-intensity-projection (MinIP) image.
 4. The method of claim 1,wherein said individually determining the visualization priority foreach of the plurality of pixels is based on a highest standard uptakevalue of each of the plurality of functional imaging data types.
 5. Themethod of claim 1, wherein said generating the joint-visualization imagecomprises using a first color-mapping scheme to represent a first of theplurality of functional imaging datasets and using a secondcolor-mapping scheme that is different than the first color-mappingscheme to represent a second of the plurality of functional imagingdatasets.
 6. The method of claim 5, further comprising: adjusting thefirst color-mapping scheme; generating an updated joint-visualizationimage using the adjusted first color-mapping scheme and the secondcolor-mapping scheme; displaying an updated joint-visualization image onthe display device.
 7. The method of claim 5, further comprising:adjusting both the first color-mapping scheme and the secondcolor-mapping scheme; generating an updated joint-visualization image onthe display device using the adjusted first color-mapping scheme and theadjusted second color-mapping scheme; and displaying the updatedjoint-visualization image on the display device.
 8. The method of claim1, wherein a first of the plurality of functional imaging datasets wasacquired using a first radiotracer and a second of the plurality offunctional imaging datasets was acquired using a second radiotracer,wherein the second radiotracer includes a different ligand than thefirst radiotracer.
 9. The method of claim 1, wherein a first of theplurality of functional imaging datasets was acquired using a firstfunctional imaging modality and a second of the plurality of functionalimaging datasets was acquired using a second functional imagingmodality, wherein the second functional imaging modality is differentthan the first functional imaging modality.
 10. The method of claim 1,further comprising: accessing an anatomical imaging dataset; whereinsaid registering the plurality of functional imaging datasets to eachother comprises registering each of the plurality of functional imagingdatasets to the anatomical imaging dataset; and generating an anatomicalimage from the anatomical imaging dataset; wherein said displaying thejoint-visualization image comprises displaying the joint-visualizationimage as an overlay on the anatomical image in a fusion mode.
 11. Aworkstation comprising: a display device; and a processor, wherein theprocess is configured to: access a plurality of functional imagingdatasets acquired from a volume-of-interest, wherein each of theplurality of functional imaging datasets is a different one of aplurality of functional imaging data types; register the plurality offunctional imaging datasets to each other; determine a visualizationpriority for each of a plurality of pixels in a joint-visualizationimage based on a logical comparison of corresponding information in eachof the plurality of functional imaging datasets, wherein thevisualization priority defines which one of the plurality of functionalimaging data types will be represented by each of the plurality ofpixels; generate the j oint-visualization image based on thevisualization priority determined for each of the plurality of pixelsand at least a portion of each of the plurality of functional imagingdatasets, wherein the joint-visualization image represents informationfrom each of the plurality of functional imaging data types at the sametime, and wherein each of the plurality of pixels in thejoint-visualization image represents only a single one of the pluralityof functional imaging data types; and display the joint-visualizationimage on the display device.
 12. The workstation of claim 11, whereinthe joint-visualization image is selected from the list consisting of amaximum-intensity-projection (MIP) image, a minimum-intensity-projection(MinIP) image, and a multiplanar reformation image.
 13. The workstationof claim 11, wherein the joint-visualization image comprises one of amaximum-intensity-projection (MIP) image or aminimum-intensity-projection (MinIP) image, and wherein the processor isconfigured to perform the logical comparison by either: comparing afirst maximum-intensity-projection (MIP) value calculated from a firstof the plurality of functional imaging datasets with a secondmaximum-intensity-projection (MIP) value calculated from a second of theplurality of functional imaging datasets when the joint-visualizationimage is a maximum-intensity projection (MIP) image; or comparing afirst minimum-intensity-projection (MinIP) value calculated from thefirst of the plurality of functional imaging datasets with a secondminimum-intensity-projection (MinIP) value calculated from the second ofthe plurality of functional imaging datasets when thejoint-visualization image is a minimum-intensity-projection (MinIP)image.
 14. The workstation of claim 11, wherein the processor isconfigured to individually determining the visualization priority foreach of the plurality of pixels based on a highest standard uptake valueof each of the plurality of functional imaging data types.
 15. Theworkstation of claim 11, wherein the processor is configured to generatethe joint-visualization image using a first color-mapping scheme torepresent a first of the plurality of functional imaging datasets andusing a second color-mapping scheme that is different than the firstcolor-mapping scheme to represent a second of the plurality offunctional imaging datasets.
 16. The workstation of claim 11, whereinthe processor is further configured to: adjust the first color-mappingscheme; generate an updated joint-visualization image using the adjustedfirst color-mapping scheme and the second color-mapping scheme; displayan updated joint-visualization image on the display device.
 17. Theworkstation of claim 11, wherein the processor is further configured to:adjust both the first color-mapping scheme and the second color-mappingscheme; generate an updated joint-visualization image on the displaydevice using the adjusted first color-mapping scheme and the adjustedsecond color-mapping scheme; and display the updated joint-visualizationimage on the display device.
 18. The workstation of claim 11, wherein afirst of the plurality of functional imaging datasets was acquired usinga first radiotracer and a second of the plurality of functional imagingdatasets was acquired using a second radiotracer, wherein the secondradiotracer includes a different ligand than the first radiotracer. 19.The workstation of claim 11, wherein a first of the plurality offunctional imaging datasets was acquired using a first functionalimaging modality and a second of the plurality of functional imagingdatasets was acquired using a second functional imaging modality,wherein the second functional imaging modality is different than thefirst functional imaging modality.
 20. The workstation of claim 11,wherein the processor is further configured to: access an anatomicalimaging dataset; wherein said registering the plurality of functionalimaging datasets to each other comprises registering each of theplurality of functional imaging datasets to the anatomical imagingdataset; and generate an anatomical image from the anatomical imagingdataset; wherein said displaying the joint-visualization image comprisesdisplaying the joint-visualization image as an overlay on the anatomicalimage in a fusion mode.